In this lecture, we'll cover the following:

• Extracting stress resultants from OpenSeesPy elements at Gauss sampling points.
• Understanding how stress resultants are stored (four Gauss points, eight components each).
• Mapping local Gauss point coordinates to global coordinates using shape functions.
• Organising and storing resultants $(Mxx, Myy, Mxy, Qx, Qy)$ with their spatial positions.
• Preparing data for later visualisation, including identifying maximum values.

In this lecture, we focus on extracting stress resultant data from each element in a finite element model using OpenSeesPy. We iterate through every element and access the stress resultants computed at the four Gauss sampling points, rather than at the nodes. We then organise these results by reshaping the data into a structured format and pairing each set of resultants with its corresponding local Gauss point coordinates.

We then use shape functions to interpolate from nodal coordinates to determine the global position of each Gauss point. This allows us to associate each stress resultant value (including bending moments and transverse shears) with a specific location in the structure. Finally, we store these values in arrays and extract key metrics such as maximum absolute values, setting up the dataset for visualisation, where handling the irregular Gauss point distribution will become important.

Next up

In the next lecture, we will visualise these moments using triangulation-based contour plots suited to the irregular Gauss point distribution.